gene regulatory networks


Summary: Interacting DNA-encoded regulatory subsystems in the GENOME that coordinate input from activator and repressor TRANSCRIPTION FACTORS during development, cell differentiation, or in response to environmental cues. The networks function to ultimately specify expression of particular sets of GENES for specific conditions, times, or locations.

Top Publications

  1. Schreiber S, Shamji A, Clemons P, Hon C, Koehler A, Munoz B, et al. Towards patient-based cancer therapeutics. Nat Biotechnol. 2010;28:904-6 pubmed publisher
  2. . De novo mutations in synaptic transmission genes including DNM1 cause epileptic encephalopathies. Am J Hum Genet. 2014;95:360-70 pubmed publisher
    ..These findings emphasize an important role for synaptic dysregulation in epileptic encephalopathies, above and beyond that caused by ion channel dysfunction...
  3. Vo N, Phan V. Exploiting dependencies of pairwise comparison outcomes to predict patterns of gene response. BMC Bioinformatics. 2014;15 Suppl 11:S2 pubmed publisher
    ..Patterns of highly-variantly expressed genes can be predicted by varying ? intelligently. Furthermore, clusters are labeled meaningfully with patterns that describe precisely how genes in such clusters respond to treatments. ..
  4. Hakala K, Van Landeghem S, Salakoski T, Van de Peer Y, Ginter F. Application of the EVEX resource to event extraction and network construction: Shared Task entry and result analysis. BMC Bioinformatics. 2015;16 Suppl 16:S3 pubmed publisher
    ..A detailed performance and error analysis provides more insight into the relatively low recall rates. ..
  5. Shchetynsky K, Diaz Gallo L, Folkersen L, Hensvold A, Catrina A, Berg L, et al. Discovery of new candidate genes for rheumatoid arthritis through integration of genetic association data with expression pathway analysis. Arthritis Res Ther. 2017;19:19 pubmed publisher
    ..Integration of RNA-seq data with findings from association studies, and consequent pathway analysis implicate new candidate genes, ERBB2, TP53 and THOP1 in the pathogenesis of RA. ..
  6. Fish J, Cantu Gutierrez M, Dang L, Khyzha N, Chen Z, Veitch S, et al. Dynamic regulation of VEGF-inducible genes by an ERK/ERG/p300 transcriptional network. Development. 2017;144:2428-2444 pubmed publisher
    ..Collectively, these findings elucidate a novel transcriptional pathway contributing to VEGF-dependent angiogenesis. ..
  7. McKay R, Hauk P, Wu H, Pottash A, Shang W, Terrell J, et al. Controlling localization of Escherichia coli populations using a two-part synthetic motility circuit: An accelerator and brake. Biotechnol Bioeng. 2017;114:2883-2895 pubmed publisher
    ..coli accumulate in pyocyanin-rich locales. We suggest that such approaches may find utility in engineering probiotics so that their beneficial functions can be focused in areas of principal benefit. ..
  8. Arpawong T, Pendleton N, Mekli K, McArdle J, Gatz M, Armoskus C, et al. Genetic variants specific to aging-related verbal memory: Insights from GWASs in a population-based cohort. PLoS ONE. 2017;12:e0182448 pubmed publisher
    ..Findings from this first U.S. population-based GWAS study conducted on both age-related immediate and delayed verbal memory merit continued examination in other samples and additional measures of verbal memory. ..
  9. Kalender Atak Z, Imrichová H, Svetlichnyy D, Hulselmans G, Christiaens V, Reumers J, et al. Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks. Genome Med. 2017;9:80 pubmed publisher
    ..cisTarget is available from . ..

More Information

Publications112 found, 100 shown here

  1. Wang X, Wen X, Zhou J, Qi Y, Wu R, Wang Y, et al. MicroRNA-223 and microRNA-21 in peripheral blood B cells associated with progression of primary biliary cholangitis patients. PLoS ONE. 2017;12:e0184292 pubmed publisher
    ..Taken together, our study offers novel perspectives on the role of miRNAs in PBC pathogenesis. ..
  2. Nelson A, Cutty S, Niini M, Stemple D, Flicek P, Houart C, et al. Global identification of Smad2 and Eomesodermin targets in zebrafish identifies a conserved transcriptional network in mesendoderm and a novel role for Eomesodermin in repression of ectodermal gene expression. BMC Biol. 2014;12:81 pubmed publisher
    ..Eomesa therefore regulates the formation of all three germ layers in the early zebrafish embryo. ..
  3. Yang W, Yoshigoe K, Qin X, Liu J, Yang J, Niemierko A, et al. Identification of genes and pathways involved in kidney renal clear cell carcinoma. BMC Bioinformatics. 2014;15 Suppl 17:S2 pubmed publisher
    ..Combining differentially expressed genes with pathway and network analyses using intelligent computational approaches provide an unprecedented opportunity to identify upstream disease causal genes and effective drug targets. ..
  4. Bouhamdani N, Joy A, Barnett D, Cormier K, Léger D, Chute I, et al. Quantitative proteomics to study a small molecule targeting the loss of von Hippel-Lindau in renal cell carcinomas. Int J Cancer. 2017;141:778-790 pubmed publisher
    ..The use of a global SILAC approach was successful in identifying novel affected signaling pathways that could be exploited for the development of new personalized therapeutic strategies to target VHL-inactivated RCCs. ..
  5. Buensuceso R, Daniel Ivad M, Kilmury S, Leighton T, Harvey H, Howell P, et al. Cyclic AMP-Independent Control of Twitching Motility in Pseudomonas aeruginosa. J Bacteriol. 2017;199: pubmed publisher
  6. Zagorski M, Tabata Y, Brandenberg N, Lutolf M, Tkacik G, Bollenbach T, et al. Decoding of position in the developing neural tube from antiparallel morphogen gradients. Science. 2017;356:1379-1383 pubmed publisher
    ..Together, our data link opposing gradient dynamics in a growing tissue to precise pattern formation. ..
  7. Chen Y, Farzadfard F, Gharaei N, Chen W, Cao J, Lu T. Randomized CRISPR-Cas Transcriptional Perturbation Screening Reveals Protective Genes against Alpha-Synuclein Toxicity. Mol Cell. 2017;68:247-257.e5 pubmed publisher
    ..Thus, high-throughput and unbiased perturbation of transcriptional networks via randomized crisprTFs can reveal complex biological phenotypes and effective disease modulators...
  8. Shibata M, Breuer C, Kawamura A, Clark N, Rymen B, Braidwood L, et al. GTL1 and DF1 regulate root hair growth through transcriptional repression of ROOT HAIR DEFECTIVE 6-LIKE 4 in Arabidopsis. Development. 2018;145: pubmed publisher
    ..This study therefore uncovers a core regulatory module that fine-tunes the extent of root hair growth by the orchestrated actions of opposing transcription factors. ..
  9. Song R, Liu Q, Liu T, Li J. Connecting rules from paired miRNA and mRNA expression data sets of HCV patients to detect both inverse and positive regulatory relationships. BMC Genomics. 2015;16 Suppl 2:S11 pubmed publisher
    ..Our rule discovery method is useful for integrating binding information and expression profile for identifying HCV miRNA-mRNA regulatory modules and can be applied to the study of the expression profiles of other complex human diseases. ..
  10. Kis Z, Pereira H, Homma T, Pedrigi R, Krams R. Mammalian synthetic biology: emerging medical applications. J R Soc Interface. 2015;12: pubmed publisher
    ..The final sections focus on the applicability of synthetic gene networks to drug discovery, drug delivery, receptor-activating gene circuits and mammalian biomanufacturing processes. ..
  11. Nalluri J, Kamapantula B, Barh D, Jain N, Bhattacharya A, Almeida S, et al. DISMIRA: Prioritization of disease candidates in miRNA-disease associations based on maximum weighted matching inference model and motif-based analysis. BMC Genomics. 2015;16 Suppl 5:S12 pubmed publisher
    ..DISMIRA can be accessed online for free at ..
  12. Cao Y, Xu W, Niu C, Bo X, Li F. NFP: An R Package for Characterizing and Comparing of Annotated Biological Networks. Biomed Res Int. 2017;2017:7457131 pubmed publisher
    ..The software shows great potential in biological network study. The open source NFP R package is freely available under the GNU General Public License v2.0 at CRAN along with the vignette. ..
  13. Ngollo M, Lebert A, Daures M, Judes G, Rifai K, Dubois L, et al. Global analysis of H3K27me3 as an epigenetic marker in prostate cancer progression. BMC Cancer. 2017;17:261 pubmed publisher
    ..Our findings point to epigenetic mark H3K27me3 as an important event in prostate carcinogenesis and progression. The results reported here provide new molecular insights into the pathogenesis of prostate cancer. ..
  14. Zhang K, Wang J, Tong T, Wu X, Nelson R, Yuan Y, et al. Loss of H2B monoubiquitination is associated with poor-differentiation and enhanced malignancy of lung adenocarcinoma. Int J Cancer. 2017;141:766-777 pubmed publisher
    ..Taken together, our findings suggest that loss of H2Bub1 may enhance malignancy and promote disease progression in lung adenocarcinoma probably through modulating multiple cancer signaling pathways. ..
  15. Lutter M, Bahl E, Hannah C, Hofammann D, Acevedo S, Cui H, et al. Novel and ultra-rare damaging variants in neuropeptide signaling are associated with disordered eating behaviors. PLoS ONE. 2017;12:e0181556 pubmed publisher
  16. Zitnik S, Zitnik M, Zupan B, Bajec M. Sieve-based relation extraction of gene regulatory networks from biological literature. BMC Bioinformatics. 2015;16 Suppl 16:S1 pubmed publisher
    ..of information extraction procedures that can directly infer more complex relational structures, such as gene regulatory networks. We develop a computational approach for extraction of gene regulatory networks from textual data...
  17. Yu D, Lim J, Wang X, Liang F, Xiao G. Enhanced construction of gene regulatory networks using hub gene information. BMC Bioinformatics. 2017;18:186 pubmed publisher
    b>Gene regulatory networks reveal how genes work together to carry out their biological functions...
  18. Gao C, Wang P, Zhao S, Zhao C, Xia H, Hou L, et al. Small RNA profiling and degradome analysis reveal regulation of microRNA in peanut embryogenesis and early pod development. BMC Genomics. 2017;18:220 pubmed publisher
    ..These findings provided new information on miRNA-mediated regulatory pathways in peanut pod, which will contribute to the comprehensive understanding of the molecular mechanisms that governing peanut embryo and early pod development. ..
  19. Li D, Pan Z, Hu G, Zhu Z, He S. Active module identification in intracellular networks using a memetic algorithm with a new binary decoding scheme. BMC Genomics. 2017;18:209 pubmed publisher
    ..The effectiveness of proposed algorithm is validated on both small and large protein interaction networks. ..
  20. Link W, Fernandez Marcos P. FOXO transcription factors at the interface of metabolism and cancer. Int J Cancer. 2017;141:2379-2391 pubmed publisher
    ..There is emerging evidence that deregulation of FOXO factors might account for the association between insulin resistance-related metabolic disorders and cancer. ..
  21. Polak P, Kim J, Braunstein L, Karlic R, Haradhavala N, Tiao G, et al. A mutational signature reveals alterations underlying deficient homologous recombination repair in breast cancer. Nat Genet. 2017;49:1476-1486 pubmed publisher
  22. Saadatpour A, Guo G, Orkin S, Yuan G. Characterizing heterogeneity in leukemic cells using single-cell gene expression analysis. Genome Biol. 2014;15:525 pubmed publisher
    ..Overall, our single-cell analysis pinpoints previously uncharacterized heterogeneity within leukemic cells and provides new insights into the molecular signatures of acute myeloid leukemia. ..
  23. Rogers W, Goyal Y, Yamaya K, Shvartsman S, Levine M. Uncoupling neurogenic gene networks in the Drosophila embryo. Genes Dev. 2017;31:634-638 pubmed publisher
    ..We suggest that Drosophila is undergoing an evolutionary transition in central nervous system (CNS)-organizing activity from the ventral midline to the neurogenic ectoderm. ..
  24. Liu Q, Jiang C, Xu J, Zhao M, Van Bortle K, Cheng X, et al. Genome-Wide Temporal Profiling of Transcriptome and Open Chromatin of Early Cardiomyocyte Differentiation Derived From hiPSCs and hESCs. Circ Res. 2017;121:376-391 pubmed publisher
  25. Lin S, Ptasinska A, Chen X, Shrestha M, Assi S, Chin P, et al. A FOXO1-induced oncogenic network defines the AML1-ETO preleukemic program. Blood. 2017;130:1213-1222 pubmed publisher
    ..Targeting of FOXO1 therefore provides a potential therapeutic strategy for elimination of stem cells at both preleukemic and leukemic stages. ..
  26. Masalia R, Bewick A, Burke J. Connectivity in gene coexpression networks negatively correlates with rates of molecular evolution in flowering plants. PLoS ONE. 2017;12:e0182289 pubmed publisher
    ..The consistency of this result across disparate taxa suggests that it holds for flowering plants in general, as opposed to being a species-specific phenomenon. ..
  27. Cilek E, Öztürk H, Gur Dedeoglu B. Construction of miRNA-miRNA networks revealing the complexity of miRNA-mediated mechanisms in trastuzumab treated breast cancer cell lines. PLoS ONE. 2017;12:e0185558 pubmed publisher
    ..The network based representation of miRNA-miRNA interactions through their shared pathways provided a better understanding of miRNA-mediated drug response and could be suggested for further characterization of miRNA functions...
  28. Hamed M, Spaniol C, Zapp A, Helms V. Integrative network-based approach identifies key genetic elements in breast invasive carcinoma. BMC Genomics. 2015;16 Suppl 5:S2 pubmed publisher
    ..This integrative approach can be applied in a similar fashion to other cancer types, complex diseases, or for studying cellular differentiation processes. ..
  29. Kiryluk K, Li Y, Moldoveanu Z, Suzuki H, Reily C, Hou P, et al. GWAS for serum galactose-deficient IgA1 implicates critical genes of the O-glycosylation pathway. PLoS Genet. 2017;13:e1006609 pubmed publisher
  30. Stevens T, Lando D, Basu S, Atkinson L, Cao Y, Lee S, et al. 3D structures of individual mammalian genomes studied by single-cell Hi-C. Nature. 2017;544:59-64 pubmed publisher
    ..By studying genes regulated by pluripotency factor and nucleosome remodelling deacetylase (NuRD), we illustrate how the determination of single-cell genome structure provides a new approach for investigating biological processes. ..
  31. Salleh M, Mazzoni G, Höglund J, Olijhoek D, Lund P, Løvendahl P, et al. RNA-Seq transcriptomics and pathway analyses reveal potential regulatory genes and molecular mechanisms in high- and low-residual feed intake in Nordic dairy cattle. BMC Genomics. 2017;18:258 pubmed publisher
  32. Nitzan M, Rehani R, Margalit H. Integration of Bacterial Small RNAs in Regulatory Networks. Annu Rev Biophys. 2017;46:131-148 pubmed publisher
    ..Finally, we discuss the competition effects in posttranscriptional regulatory networks that may arise over shared targets, shared regulators, and shared resources and how they may lead to signal propagation across the network. ..
  33. Filiz E, Vatansever R, Ozyigit I, Uras M, Sen U, Anjum N, et al. Genome-wide identification and expression profiling of EIL gene family in woody plant representative poplar (Populus trichocarpa). Arch Biochem Biophys. 2017;627:30-45 pubmed publisher
    ..Furthermore, expression profile analysis indicated the possibility of a crosstalk between EIN3- and EIL3-like genes, and co-expression networks implicated their interactions with very diverse panels of biological molecules. ..
  34. Herzog V, Reichholf B, Neumann T, Rescheneder P, Bhat P, Burkard T, et al. Thiol-linked alkylation of RNA to assess expression dynamics. Nat Methods. 2017;14:1198-1204 pubmed publisher
    ..SLAM seq facilitates the dissection of fundamental mechanisms that control gene expression in an accessible, cost-effective and scalable manner. ..
  35. Li Z, Chen J, Yu H, He L, Xu Y, Zhang D, et al. Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia. Nat Genet. 2017;49:1576-1583 pubmed publisher
    ..Together, our findings provide novel insight into the genetic architecture and biological etiology of schizophrenia...
  36. Rustad T, Minch K, Ma S, Winkler J, Hobbs S, Hickey M, et al. Mapping and manipulating the Mycobacterium tuberculosis transcriptome using a transcription factor overexpression-derived regulatory network. Genome Biol. 2014;15:502 pubmed
    ..tuberculosis, and demonstrated the utility of this resource. These results will stimulate additional systems-level and hypothesis-driven efforts to understand M. tuberculosis adaptations that promote disease. ..
  37. Taskesen E, Staal F, Reinders M. An integrated approach of gene expression and DNA-methylation profiles of WNT signaling genes uncovers novel prognostic markers in acute myeloid leukemia. BMC Bioinformatics. 2015;16 Suppl 4:S4 pubmed publisher
    ..Our results provide novel insights in WNT signaling in AML, we discovered new and previously identified prognostic markers and a refinement of the patient groups. ..
  38. Röll S, Härtle S, Lütteke T, Kaspers B, Härtle S. Tissue and time specific expression pattern of interferon regulated genes in the chicken. BMC Genomics. 2017;18:264 pubmed publisher
    ..We confirmed many known IRGs and established a multitude of so far undescribed ones, thus providing a large database for future research on antiviral mechanisms and additional IFN functions in non-mammalian species. ..
  39. Nam S. Cancer Transcriptome Dataset Analysis: Comparing Methods of Pathway and Gene Regulatory Network-Based Cluster Identification. OMICS. 2017;21:217-224 pubmed publisher
    ..These observations collectively inform future research on cancer transcriptomics, drug discovery, and rational development of new analysis tools for optimal harnessing of omics data. ..
  40. Rowland M, Abdelzaher A, Ghosh P, Mayo M. Crosstalk and the Dynamical Modularity of Feed-Forward Loops in Transcriptional Regulatory Networks. Biophys J. 2017;112:1539-1550 pubmed publisher
  41. Liu X, Wang W, Song G, Wei X, Zeng Y, Han P, et al. Astragaloside IV ameliorates diabetic nephropathy by modulating the mitochondrial quality control network. PLoS ONE. 2017;12:e0182558 pubmed publisher
    ..In conclusion, administration of AS-IV could retard DN progression in type 2 diabetes mice, which might be associated with restoration of the mitochondrial quality control network. ..
  42. Zhou Z, Liu S, Zhang M, Zhou R, Liu J, Chang Y, et al. Overexpression of Topoisomerase 2-Alpha Confers a Poor Prognosis in Pancreatic Adenocarcinoma Identified by Co-Expression Analysis. Dig Dis Sci. 2017;62:2790-2800 pubmed publisher
    ..TOP2A was identified in association with the progression and prognosis of PDAC probably by regulating cell cycle and p53 signaling pathway. ..
  43. Jeong H, Kim S, Wee K, Sohn K. Investigating the utility of clinical outcome-guided mutual information network in network-based Cox regression. BMC Syst Biol. 2015;9 Suppl 1:S8 pubmed publisher
    ..We expect that a modification of the network regularization term in the Net-Cox model could further improve its prediction power because the properties of our network edges are not optimally reflected in its current form. ..
  44. Yamamoto S, Ohnishi M. Glucose-Specific Enzyme IIA of the Phosphoenolpyruvate:Carbohydrate Phosphotransferase System Modulates Chitin Signaling Pathways in Vibrio cholerae. J Bacteriol. 2017;199: pubmed publisher
    ..This work represents a newly identified connection between the PTS and chitin signaling pathways in V. cholerae and suggests a strategy whereby this bacterium can physiologically adapt to the existing nutrient status. ..
  45. Jingting S, Qin X, Yanju S, Ming Z, Yunjie T, Gaige J, et al. Oxidative and glycolytic skeletal muscles show marked differences in gene expression profile in Chinese Qingyuan partridge chickens. PLoS ONE. 2017;12:e0183118 pubmed publisher
    ..05). Our study demonstrates strong transcriptome differences between oxidative and glycolytic myofibers, and the results suggest that PPARGC1A is a key gene involved in chicken myofiber composition and transition. ..
  46. Ganegoda G, Wang J, Wu F, Li M. Prediction of disease genes using tissue-specified gene-gene network. BMC Syst Biol. 2014;8 Suppl 3:S3 pubmed publisher
    ..Hence it is better to use tissue-specific gene-gene network whenever possible. In addition the proposed method is a better way of constructing tissue-specific gene-gene networks. ..
  47. Yue Z, Wan P, Huang H, Xie Z, Chen J. SLDR: a computational technique to identify novel genetic regulatory relationships. BMC Bioinformatics. 2014;15 Suppl 11:S1 pubmed publisher
    ..SLDR is also computationally efficient with o(N²) complexity. In summary, we believe SLDR can be applied to the mining of functional genomics big data for future network biology and network medicine applications. ..
  48. Shimada Sugimoto M, Otowa T, Miyagawa T, Umekage T, Kawamura Y, Bundo M, et al. Epigenome-wide association study of DNA methylation in panic disorder. Clin Epigenetics. 2017;9:6 pubmed publisher
  49. Zhang R, Ehigie J, Hou X, You X, Yuan C. Steady-State-Preserving Simulation of Genetic Regulatory Systems. Comput Math Methods Med. 2017;2017:2729683 pubmed publisher
    ..Moreover, for nonstiff genetic regulatory systems, the expRK methods are more efficient than some traditional exponential RK integrators in the scientific literature. ..
  50. Hackenberg M, Langenberger D, Schwarz A, Erhart J, Kotsyfakis M. In silico target network analysis of de novo-discovered, tick saliva-specific microRNAs reveals important combinatorial effects in their interference with vertebrate host physiology. RNA. 2017;23:1259-1269 pubmed publisher
    ..As such, the herein described miRNAs may support future drug discovery and development projects that will also experimentally question their predicted molecular targets in the vertebrate host. ..
  51. Li L, Zhao W, Tao S, Li J, Xu S, Wang J, et al. Comprehensive long non-coding RNA expression profiling reveals their potential roles in systemic lupus erythematosus. Cell Immunol. 2017;319:17-27 pubmed publisher
    ..Differential lncRNAs might also function through their ceRNAs. Our study established that the aberrant expression profiles of lncRNAs may play a role in SLE and thus warrant further investigation. ..
  52. Gómez Herreros F, Margaritis T, Rodríguez Galán O, Pelechano V, Begley V, Millán Zambrano G, et al. The ribosome assembly gene network is controlled by the feedback regulation of transcription elongation. Nucleic Acids Res. 2017;45:9302-9318 pubmed publisher
    ..On the whole, this work uncovers a feedback control of ribosome biogenesis by fine-tuning transcription elongation in ribosome assembly factor-coding genes. ..
  53. Kan J, Liu T, Ma N, Li H, Li X, Wang J, et al. Transcriptome analysis of Callery pear (Pyrus calleryana) reveals a comprehensive signalling network in response to Alternaria alternata. PLoS ONE. 2017;12:e0184988 pubmed publisher
    ..A good understanding of the molecular response to this disease will allow the development of durable and environmentally friendly control strategies...
  54. Creixell P, Reimand J, Haider S, Wu G, Shibata T, Vazquez M, et al. Pathway and network analysis of cancer genomes. Nat Methods. 2015;12:615-621 pubmed publisher
    ..We provide an overview of these analysis techniques and show where they guide mechanistic and translational investigations. ..
  55. Dalan A, Gulluoglu S, Tuysuz E, Kuskucu A, Yaltırık C, Ozturk O, et al. Simultaneous analysis of miRNA-mRNA in human meningiomas by integrating transcriptome: A relationship between PTX3 and miR-29c. BMC Cancer. 2017;17:207 pubmed publisher
    ..miR-29c-3p and PTX3 are inversely correlated in tissues and meningioma cells, hinting that PTX3 can be regulated by miR-29c-3p. Furthermore, we determined potential clinicopathological markers. ..
  56. Rana A, Jain S, Puri N, Kaw M, Sirianni N, Eren D, et al. The transcriptional regulation of the human angiotensinogen gene after high-fat diet is haplotype-dependent: Novel insights into the gene-regulatory networks and implications for human hypertension. PLoS ONE. 2017;12:e0176373 pubmed publisher
  57. Lu L, Zeng J. Evaluation of K-ras and p53 expression in pancreatic adenocarcinoma using the cancer genome atlas. PLoS ONE. 2017;12:e0181532 pubmed publisher
    ..As in the data mining study in the TCGA databases, our study provides a new perspective to understand the genetic features of K-ras and p53 in pancreatic adenocarcinoma. ..
  58. Yang Z, Zhang Y, Jiang Y, Zhu F, Zeng L, Wang Y, et al. Transcriptional responses in the hepatopancreas of Eriocheir sinensis exposed to deltamethrin. PLoS ONE. 2017;12:e0184581 pubmed publisher
    ..sinensis for the first time and will help in understanding the toxicity and molecular metabolic mechanisms of deltamethrin in E. sinensis. ..
  59. Huang K, Yang K, Lin H, Tsao T, Lee S. Transcriptome alterations of mitochondrial and coagulation function in schizophrenia by cortical sequencing analysis. BMC Genomics. 2014;15 Suppl 9:S6 pubmed publisher
    ..The accuracy of candidate genes evaluated from different quantification tools could be improved by crosstalk analysis of overlapping genes in genetic networks. ..
  60. Brown W. Exercise-associated DNA methylation change in skeletal muscle and the importance of imprinted genes: a bioinformatics meta-analysis. Br J Sports Med. 2015;49:1567-78 pubmed publisher
    ..Imprinted genes were identified in skeletal muscle gene networks and exercise-associated DNA methylation change. Exercise-associated DNA methylation modification could rewind the 'epigenetic clock' as we age. CRD42014009800. ..
  61. Bentham R, Bryson K, Szabadkai G. MCbiclust: a novel algorithm to discover large-scale functionally related gene sets from massive transcriptomics data collections. Nucleic Acids Res. 2017;45:8712-8730 pubmed publisher
    ..The identified massive biclusters can be used to develop improved transcriptomics based diagnosis tools for diseases caused by altered gene expression, or used for further network analysis to understand genotype-phenotype correlations. ..
  62. Kong D, Thorsen T, Babb J, Wick S, Gam J, Weiss R, et al. Open-source, community-driven microfluidics with Metafluidics. Nat Biotechnol. 2017;35:523-529 pubmed publisher
    ..Metafluidics is intended to enable a broad community of engineers, DIY enthusiasts, and other nontraditional participants with limited fabrication skills to contribute to microfluidic research. ..
  63. Arshad O, Venkatasubramani P, Datta A, Venkatraj J. Using Boolean Logic Modeling of Gene Regulatory Networks to Exploit the Links Between Cancer and Metabolism for Therapeutic Purposes. IEEE J Biomed Health Inform. 2016;20:399-407 pubmed publisher
  64. Foroushani A, Agrahari R, Docking R, Chang L, Duns G, Hudoba M, et al. Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications. BMC Med Genomics. 2017;10:16 pubmed publisher
    ..These signatures provide valuable information about the underlying biology of diseases, and they are useful in predicting diagnosis and prognosis. ..
  65. Trinh L, Chong Morrison V, Gavriouchkina D, Hochgreb Hägele T, Senanayake U, Fraser S, et al. Biotagging of Specific Cell Populations in Zebrafish Reveals Gene Regulatory Logic Encoded in the Nuclear Transcriptome. Cell Rep. 2017;19:425-440 pubmed publisher
    ..By eliminating background inherent to complex embryonic environments, biotagging allows analyses of molecular interactions at high resolution. ..
  66. Gallenne T, Ross K, Visser N, Salony -, Desmet C, Wittner B, et al. Systematic functional perturbations uncover a prognostic genetic network driving human breast cancer. Oncotarget. 2017;8:20572-20587 pubmed publisher
    ..We propose that pharmacological inhibition of components within this network, such as PAICS, may be used in conjunction with the Fra-1 prognostic classifier towards personalized management of poor prognosis breast cancer. ..
  67. Hu Y, Xin J, Hu Y, Zhang L, Wang J. Analyzing the genes related to Alzheimer's disease via a network and pathway-based approach. Alzheimers Res Ther. 2017;9:29 pubmed publisher
    ..In addition, the framework proposed in this study could be used to investigate the pathological molecular network and genes relevant to other complex diseases or phenotypes. ..
  68. Wang S, Tang Z, Chen C, Shimada M, Koche R, Wang L, et al. A UTX-MLL4-p300 Transcriptional Regulatory Network Coordinately Shapes Active Enhancer Landscapes for Eliciting Transcription. Mol Cell. 2017;67:308-321.e6 pubmed publisher
  69. Wren J, Dozmorov M, Burian D, Perkins A, Zhang C, Hoyt P, et al. Proceedings of the 2014 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference. BMC Bioinformatics. 2014;15 Suppl 11:I1 pubmed publisher
  70. Stoney R, Ames R, Nenadic G, Robertson D, Schwartz J. Disentangling the multigenic and pleiotropic nature of molecular function. BMC Syst Biol. 2015;9 Suppl 6:S3 pubmed publisher
    ..The pathway network demonstrates the cooperation of multiple pathways to perform biological processes and organises pathways into functionally related clusters with interdependent outcomes. ..
  71. Wang F, Li Y, Wu X, Yang M, Cong W, Fan Z, et al. Transcriptome analysis of coding and long non-coding RNAs highlights the regulatory network of cascade initiation of permanent molars in miniature pigs. BMC Genomics. 2017;18:148 pubmed publisher
    ..Our data provide fundamental knowledge and a basis for understanding the molecular mechanisms governing cascade initiation of additional molars, but also provide an important resource for developmental biology research. ..
  72. Kleinman C, Sycz G, Bonomi H, Rodríguez R, Zorreguieta A, Sieira R. ChIP-seq analysis of the LuxR-type regulator VjbR reveals novel insights into the Brucella virulence gene expression network. Nucleic Acids Res. 2017;45:5757-5769 pubmed publisher
    ..Taken together, our results bring new insights into the extent and functionality of LuxR-type-related transcriptional networks. ..
  73. Hutchins A, Yang Z, Li Y, He F, Fu X, Wang X, et al. Models of global gene expression define major domains of cell type and tissue identity. Nucleic Acids Res. 2017;45:2354-2367 pubmed publisher
    ..This model has implications for understanding trans-lineage differentiation for stem cells, developmental cell biology and regenerative medicine. ..
  74. Li S, Sullivan N, Rouphael N, Yu T, Banton S, Maddur M, et al. Metabolic Phenotypes of Response to Vaccination in Humans. Cell. 2017;169:862-877.e17 pubmed publisher
    ..Our approach is broadly applicable to study human immunity and can help to identify predictors of efficacy as well as mechanisms controlling immunity to vaccination. ..
  75. Coker E, Mitsopoulos C, Workman P, Al Lazikani B. SiGNet: A signaling network data simulator to enable signaling network inference. PLoS ONE. 2017;12:e0177701 pubmed publisher
    ..SiGNet can also be used to produce preliminary models of key biological pathways following perturbation. ..
  76. Sulli M, Mandolino G, Sturaro M, Onofri C, Diretto G, Parisi B, et al. Molecular and biochemical characterization of a potato collection with contrasting tuber carotenoid content. PLoS ONE. 2017;12:e0184143 pubmed publisher
    ..Other tuber metabolites, such as glucose, monogalactosyldiacyglycerol (a glycolipid), or suberin precursors, showed instead significant correlations with both traits. ..
  77. Gursky V, Kozlov K, Kulakovskiy I, Zubair A, Marjoram P, Lawrie D, et al. Translating natural genetic variation to gene expression in a computational model of the Drosophila gap gene regulatory network. PLoS ONE. 2017;12:e0184657 pubmed publisher
    ..Taken together, these results provide a systems-level view of how genetic variation translates to the level of gene regulatory networks via combinatorial SNP effects.
  78. Penfold C, Shifaz A, Brown P, Nicholson A, Wild D. CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data. Stat Appl Genet Mol Biol. 2015;14:307-10 pubmed publisher
    ..the causal structure identification (CSI) package, a Gaussian process based approach to inferring gene regulatory networks (GRNs) from multiple time series data...
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